Analysis overview
This systematic review and meta-analysis evaluated whether antipsychotic treatment reverses NMDA receptor antagonist–induced deficits in social preference in animal models. Effect sizes were calculated as Hedges’ g and synthesized using multilevel random-effects models to account for dependency between multiple outcomes within experiments and studies.
Study landscape and evidence distribution
Alluvial plot
Distribution of evidence across species, NMDA antagonists, and
antipsychotics. Alluvial plot illustrating how effect sizes are
distributed across animal species, NMDA receptor antagonists used to
induce social deficits, and antipsychotic drugs tested for reversal.
Evidence maps
Evidence maps of experimental design
characteristics.
Bubble size represents the number of effect sizes (k), and color
indicates the mean Hedges’ g within each cell.
Main meta-analysis
Overall effect
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.659 1.288 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Heterogeneity:
## Q(df = 39) = 353.192, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## 0.940 0.302 3.115 19.88 0.005 0.310 1.569 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t-test and confidence interval, df: Satterthwaite approx)Multilevel random-effects meta-analysis with robust variance estimation.
Orchard plot
Overall antipsychotic effect on reversing NMDA antagonist–induced social preference deficits. Orchard plot summarizing study-level pooled effects with multilevel heterogeneity.
Prediction interval for the overall effect
## estimate ci_lb ci_ub pi_lb pi_ub
## 1 0.9397334 0.3101173 1.569349 -1.821002 3.700469Prediction interval for the overall effect. The 95% prediction interval reflects expected variability in the true effect size of a future study beyond sampling error.
## Component I.....
## 1 I2_Total 85.2
## 2 I2_study_id 85.2
## 3 I2_study_id/exp_id 0.0Multilevel heterogeneity estimates (I²).
Publication bias
Funnel plots
Funnel plot using standard error.
Funnel plot using inverse square root of total sample size.
Precision Effect Test (PET)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.790 0.889 21 no study_id
## sigma^2.2 0.125 0.354 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 38) = 114.214, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 3.88) = 94.901, p-val < .001
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## intrcpt -4.054 0.686 -5.913 5.5 0.001 -5.770 -2.338 **
## sqrt(vi) 8.514 0.874 9.742 3.88 <.001 6.058 10.970 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)PET (Precision Effect Test) model with robust variance estimation. The PET model evaluates small-study bias by regressing effect size on study precision.
Precision Effect Estimate with Standard Error (PEESE)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.680 0.825 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 38) = 100.556, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 2.88) = 36.592, p-val = 0.010
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## intrcpt -0.748 0.433 -1.727 12.15 0.110 -1.690 0.195
## vi 4.678 0.773 6.049 2.88 0.010 2.156 7.201 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)PEESE (Precision Effect Estimate with Standard Error) model with robust variance estimation. The PEESE model provides an alternative bias-adjusted estimate using study variance.
Time-lag bias
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.666 1.291 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 38) = 337.279, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 7.03) = 2.130, p-val = 0.188
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## intrcpt 0.916 0.292 3.133 18.17 0.006 0.302 1.530 **
## year_c 0.104 0.071 1.459 7.03 0.188 -0.064 0.273
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)Time-lag meta-regression model. This model tests whether effect sizes change systematically over publication time. A significant slope would indicate temporal trends such as decline or inflation of reported effects.
Time-lag bias: effect size as a function of publication year.
Moderators
Moderator analyses were conducted using multilevel meta-analytic models with robust variance estimation to examine whether effect sizes differed across experimental and biological characteristics. Orchard plots display pooled effects for each moderator level, with study-level clustering and multilevel heterogeneity taken into account.
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 2.885 1.699 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 33) = 223.183, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 2:7):¹
## F(df1 = 6, df2 = 0) = 0.000, p-val = NA
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## intrcpt 1.622 0.000 4497038.493 3.75 <.001 1.622
## atp_drugAripiprazole 0.289 0.581 0.498 7.97 0.632 -1.051
## atp_drugClozapine -0.503 0.781 -0.644 7.44 0.539 -2.329
## atp_drugHaloperidol -4.301 0.955 -4.505 6.36 0.004 -6.606
## atp_drugOlanzapine 0.710 0.731 0.971 7.14 0.363 -1.012
## atp_drugRisperidone 0.373 0.583 0.640 15.99 0.531 -0.863
## atp_drugSulpiride -1.884 0.881 -2.137 5.98 0.077 -4.043
## ci.ub¹
## intrcpt 1.622 ***
## atp_drugAripiprazole 1.629
## atp_drugClozapine 1.322
## atp_drugHaloperidol -1.997 **
## atp_drugOlanzapine 2.433
## atp_drugRisperidone 1.608
## atp_drugSulpiride 0.275 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.791 1.338 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 37) = 328.263, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 1:3):¹
## F(df1 = 3, df2 = 2.69) = 2.657, p-val = 0.237
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## atp_scheduleAcute 0.581 0.346 1.679 4.98 0.154 -0.310 1.471
## atp_scheduleRepeated 1.154 0.428 2.695 12.93 0.018 0.229 2.080
## atp_scheduleUnclear 0.298 0.153 1.951 1 0.302 -1.644 2.240
##
## atp_scheduleAcute
## atp_scheduleRepeated *
## atp_scheduleUnclear
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.697 1.303 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 36) = 349.602, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 1:4):¹
## F(df1 = 4, df2 = 0.66) = 0.505, p-val = 0.787
##
## Model Results:
##
## estimate se¹ tval¹ df¹
## atp_administration_routeImmersion 0.020 8.793 0.002 1
## atp_administration_routeIntraperitoneal 0.864 0.348 2.481 11.56
## atp_administration_routeOral 1.180 0.592 1.994 7.21
## atp_administration_routeUnclear 0.928 0.660 1.406 1
## pval¹ ci.lb¹ ci.ub¹
## atp_administration_routeImmersion 0.999 -111.702 111.743
## atp_administration_routeIntraperitoneal 0.030 0.102 1.627 *
## atp_administration_routeOral 0.085 -0.211 2.572 .
## atp_administration_routeUnclear 0.394 -7.461 9.318
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.530 1.237 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 37) = 310.765, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 1:3):¹
## F(df1 = 3, df2 = 8.63) = 3.528, p-val = 0.064
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## nmda_antagonistKetamine 1.291 0.541 2.387 8.95 0.041 0.066
## nmda_antagonistMK-801 0.131 0.115 1.136 4.99 0.307 -0.166
## nmda_antagonistPhencyclidine 1.280 0.521 2.459 3.97 0.070 -0.170
## ci.ub¹
## nmda_antagonistKetamine 2.515 *
## nmda_antagonistMK-801 0.428
## nmda_antagonistPhencyclidine 2.730 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.819 1.349 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 37) = 326.713, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 1:3):¹
## F(df1 = 3, df2 = 1.83) = 1.558, p-val = 0.426
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## speciesMouse 1.062 0.362 2.932 15.93 0.010 0.294 1.831
## speciesRat 0.618 0.567 1.091 1.98 0.390 -1.839 3.075
## speciesZebrafish 0.020 9.163 0.002 1 0.999 -116.410 116.451
##
## speciesMouse **
## speciesRat
## speciesZebrafish
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.776 1.333 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 37) = 345.491, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 1:3):¹
## F(df1 = 3, df2 = 5.79) = 19.330, p-val = 0.002
##
## Model Results:
##
## estimate se¹ tval¹
## developmental_stage_inductionAdult 1.119 0.384 2.913
## developmental_stage_inductionJuvenile/Adolescent 0.521 0.666 0.782
## developmental_stage_inductionUnclear 1.293 0.156 8.302
## df¹ pval¹ ci.lb¹
## developmental_stage_inductionAdult 9.93 0.016 0.262
## developmental_stage_inductionJuvenile/Adolescent 5.97 0.464 -1.111
## developmental_stage_inductionUnclear 1.99 0.014 0.620
## ci.ub¹
## developmental_stage_inductionAdult 1.976 *
## developmental_stage_inductionJuvenile/Adolescent 2.154
## developmental_stage_inductionUnclear 1.966 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 40; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 5.257 2.293 21 no study_id
## sigma^2.2 0.000 0.000 28 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 27) = 145.522, p-val < .001
##
## Number of estimates: 40
## Number of clusters: 21
## Estimates per cluster: 1-5 (mean: 1.90, median: 2)
##
## Test of Moderators (coefficients 2:13):¹
## F(df1 = 12, df2 = 0) = 0.000, p-val = NA
##
## Model Results:
##
## estimate se¹
## intrcpt 1.622 0.000
## atp_nmda_interactionClozapine × Ketamine 0.831 0.663
## atp_nmda_interactionHaloperidol × Ketamine -4.871 1.434
## atp_nmda_interactionOlanzapine × Ketamine 1.213 2.704
## atp_nmda_interactionRisperidone × Ketamine 1.004 0.707
## atp_nmda_interactionAripiprazole × MK-801 -0.220 1.257
## atp_nmda_interactionHaloperidol × MK-801 -7.669 2.212
## atp_nmda_interactionOlanzapine × MK-801 0.130 1.399
## atp_nmda_interactionRisperidone × MK-801 -0.237 1.248
## atp_nmda_interactionSulpiride × MK-801 -4.964 2.238
## atp_nmda_interactionClozapine × Phencyclidine 0.441 0.433
## atp_nmda_interactionHaloperidol × Phencyclidine -1.525 0.409
## atp_nmda_interactionOlanzapine × Phencyclidine -1.824 0.000
## tval¹ df¹ pval¹
## intrcpt 3039847.782 5.64 <.001
## atp_nmda_interactionClozapine × Ketamine 1.254 4.27 0.274
## atp_nmda_interactionHaloperidol × Ketamine -3.397 4.2 0.025
## atp_nmda_interactionOlanzapine × Ketamine 0.449 1 0.732
## atp_nmda_interactionRisperidone × Ketamine 1.420 6.83 0.200
## atp_nmda_interactionAripiprazole × MK-801 -0.175 4.8 0.868
## atp_nmda_interactionHaloperidol × MK-801 -3.468 3.27 0.035
## atp_nmda_interactionOlanzapine × MK-801 0.093 4.75 0.930
## atp_nmda_interactionRisperidone × MK-801 -0.190 4.73 0.857
## atp_nmda_interactionSulpiride × MK-801 -2.218 3.37 0.103
## atp_nmda_interactionClozapine × Phencyclidine 1.018 3 0.384
## atp_nmda_interactionHaloperidol × Phencyclidine -3.729 2.97 0.034
## atp_nmda_interactionOlanzapine × Phencyclidine -15188416.334 5.56 <.001
## ci.lb¹ ci.ub¹
## intrcpt 1.622 1.622 ***
## atp_nmda_interactionClozapine × Ketamine -0.965 2.628
## atp_nmda_interactionHaloperidol × Ketamine -8.780 -0.962 *
## atp_nmda_interactionOlanzapine × Ketamine -33.149 35.575
## atp_nmda_interactionRisperidone × Ketamine -0.676 2.685
## atp_nmda_interactionAripiprazole × MK-801 -3.493 3.053
## atp_nmda_interactionHaloperidol × MK-801 -14.395 -0.943 *
## atp_nmda_interactionOlanzapine × MK-801 -3.522 3.783
## atp_nmda_interactionRisperidone × MK-801 -3.501 3.026
## atp_nmda_interactionSulpiride × MK-801 -11.667 1.739
## atp_nmda_interactionClozapine × Phencyclidine -0.937 1.818
## atp_nmda_interactionHaloperidol × Phencyclidine -2.835 -0.216 *
## atp_nmda_interactionOlanzapine × Phencyclidine -1.824 -1.824 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
Meta-regression
Cumulative exposure
##
## Multivariate Meta-Analysis Model (k = 34; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.833 1.354 19 no study_id
## sigma^2.2 0.000 0.000 26 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 292.990, p-val < .001
##
## Number of estimates: 34
## Number of clusters: 19
## Estimates per cluster: 0-5 (mean: 1.62, median: 1)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 1.14) = 0.003, p-val = 0.963
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## intrcpt 1.028 0.334 3.076 17.61 0.007 0.325
## atp_cumulative_exposure 0.000 0.002 0.057 1.14 0.963 -0.022
## ci.ub¹
## intrcpt 1.730 **
## atp_cumulative_exposure 0.022
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
Meta-regression of cumulative exposure versus effect size. The regression coefficient indicates whether increasing cumulative exposure is associated with changes in effect size, suggesting a potential dose–response relationship.
Log-transformed cumulative exposure
##
## Multivariate Meta-Analysis Model (k = 34; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 1.857 1.363 19 no study_id
## sigma^2.2 0.000 0.000 26 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 32) = 291.254, p-val < .001
##
## Number of estimates: 34
## Number of clusters: 19
## Estimates per cluster: 0-5 (mean: 1.62, median: 1)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 1.58) = 0.334, p-val = 0.635
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## intrcpt 1.145 0.387 2.958 13.06 0.011 0.309
## log_atp_cumulative_exposure -0.133 0.230 -0.578 1.58 0.635 -1.424
## ci.ub¹
## intrcpt 1.981 *
## log_atp_cumulative_exposure 1.158
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
Meta-regression of log-transformed cumulative exposure. The log-transformed model evaluates potential non-linear exposure–effect relationships and the robustness of the association.
Sensitivity analyses
Rho sensitivity
## rho estimate ci
## 1 0.0 1.1634533 [0.66, 1.667]
## 2 0.3 1.0519299 [0.52, 1.584]
## 3 0.5 0.9397334 [0.348, 1.531]
## 4 0.8 0.5965782 [-0.237, 1.43]
Sensitivity of the overall effect to within-study correlation (rho). This analysis evaluates the robustness of the pooled effect size to assumptions about the correlation between multiple effect sizes within the same experiment. Each row reports the overall effect estimate (Hedges’ g) and 95% confidence interval obtained under a different assumed value of rho. Stability of estimates across rho values indicates robustness to within-study dependency assumptions.
Leave-one-study-out
## left_out_study estimate ci_lb ci_ub
## 1 araujo_2016 0.8814916 0.2738560 1.489127
## 2 araujo_2021 0.8398390 0.2546932 1.424985
## 3 ben-azu_2018 1.0718716 0.5300165 1.613727
## 4 ben-azu_2018b 0.7676359 0.2557572 1.279514
## 5 ben-azu_2018c 0.9975420 0.3814165 1.613667
## 6 deiana_2015 1.0140873 0.4062520 1.621923
## 7 gil-ad_2014 0.9948057 0.3837137 1.605898
## 8 jeong_2022 0.9750287 0.3543764 1.595681
## 9 ju_2020 0.9747979 0.3544917 1.595104
## 10 koo_2019 0.9752219 0.3546167 1.595827
## 11 monte_2013 0.9288168 0.3038531 1.553780
## 12 nikiforuk_2016 0.9099172 0.2930953 1.526739
## 13 oshodi_2021 0.8712722 0.2660169 1.476528
## 14 sanavi_2022 0.9519570 0.3269470 1.576967
## 15 seibt_2011 0.9907319 0.3722333 1.609230
## 16 tadmor_2018 0.8964300 0.2812023 1.511658
## 17 tran_2016 0.9504091 0.3273866 1.573432
## 18 tran_2016b 0.9352433 0.3136109 1.556876
## 19 tran_2018 0.8474022 0.2630749 1.431730
## 20 vasconcelos_2015 0.9848004 0.3695580 1.600043
## 21 xue_2017 0.9761117 0.3562122 1.596011Leave-one-study-out analysis. Each row reports the pooled effect size (Hedges’ g) and 95% confidence interval obtained after excluding one study at a time from the meta-analysis. This analysis evaluates the influence of individual studies on the overall estimate; substantial changes after removal of a study would indicate disproportionate influence.
Excluding high risk of bias
##
## Multivariate Meta-Analysis Model (k = 11; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.405 0.636 8 no study_id
## sigma^2.2 0.000 0.000 9 no study_id/exp_id
##
## Test for Heterogeneity:
## Q(df = 10) = 44.550, p-val < .001
##
## Number of estimates: 11
## Number of clusters: 8
## Estimates per cluster: 0-2 (mean: 0.52, median: 0)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## 0.790 0.298 2.654 6.69 0.034 0.080 1.501 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t-test and confidence interval, df: Satterthwaite approx)
Overall effect excluding high risk-of-bias studies. This sensitivity analysis re-estimates the overall meta-analytic effect after excluding studies classified as having high risk of bias. The purpose of this analysis is to assess whether the pooled effect estimate is robust to the exclusion of potentially biased evidence.
Annex: Individual effect sizes included in the meta-analysis
Calculated effect sizes
##
## study effect_id species nmda_antagonist atp_drug hedges_g
## 1 Ben-Azu 2018 5 Mouse Ketamine Risperidone 6.972
## 2 Ben-Azu 2018 4 Mouse Ketamine Haloperidol -1.960
## 3 Nikiforuk 2016 25 Rat Ketamine Amisulpride 1.622
## 4 Ben-Azu 2018 b 110 Mouse Ketamine Risperidone 3.836
## 5 Ben-Azu 2018 b 111 Mouse Ketamine Risperidone 6.249
## 6 Deiana 2015 118 Rat MK-801 Risperidone -0.301
## 7 Deiana 2015 115 Rat MK-801 Aripiprazole -0.448
## 8 Deiana 2015 119 Rat MK-801 Olanzapine -0.175
## 9 Deiana 2015 116 Rat MK-801 Aripiprazole -0.332
## 10 Deiana 2015 117 Rat MK-801 Aripiprazole -0.437
## 11 Seibt 2011 155 Zebrafish MK-801 Haloperidol 0.519
## 12 Seibt 2011 156 Zebrafish MK-801 Sulpiride 3.480
## 13 Seibt 2011 157 Zebrafish MK-801 Olanzapine 11.674
## 14 Tadmor 2018 161 Mouse Phencyclidine Clozapine 1.830
## 15 Tran 2018 162 Mouse Phencyclidine Clozapine 3.206
## 16 Ben-Azu 2018 c 167 Mouse Ketamine Risperidone 5.500
## 17 Ben-Azu 2018 c 166 Mouse Ketamine Haloperidol 0.157
## 18 Ben-Azu 2018 c 169 Mouse Ketamine Risperidone 5.285
## 19 Ben-Azu 2018 c 168 Mouse Ketamine Haloperidol -0.108
## 20 Araujo 2021 170 Mouse Ketamine Olanzapine 2.246
## 21 Araujo 2021 171 Mouse Ketamine Olanzapine 3.874
## 22 Ju 2020 174 Mouse MK-801 Risperidone 0.086
## 23 Ju 2020 175 Mouse MK-801 Aripiprazole 0.333
## 24 Ju 2020 173 Mouse MK-801 Olanzapine 0.489
## 25 Koo 2019 216 Mouse MK-801 Aripiprazole 0.298
## 26 Oshodi 2021 229 Mouse Ketamine Risperidone 2.142
## 27 Oshodi 2021 230 Mouse Ketamine Risperidone 2.394
## 28 Araujo 2016 270 Mouse Ketamine Risperidone 2.178
## 29 Sanavi 2022 272 Rat Ketamine Risperidone 0.778
## 30 Sanavi 2022 271 Rat Ketamine Clozapine 0.746
## 31 Gil-Ad 2014 298 Mouse Phencyclidine Olanzapine -0.203
## 32 Tran 2016 337 Mouse Phencyclidine Haloperidol 0.234
## 33 Tran 2016 336 Mouse Phencyclidine Clozapine 2.208
## 34 Tran 2016 b 338 Mouse Phencyclidine Clozapine 1.086
## 35 Jeong 2022 414 Mouse MK-801 Aripiprazole 0.302
## 36 Monte 2013 415 Mouse Ketamine Risperidone 1.082
## 37 Monte 2013 416 Mouse Ketamine Risperidone 1.309
## 38 Vasconcelos 2015 472 Mouse Ketamine Clozapine 2.476
## 39 Vasconcelos 2015 473 Mouse Ketamine Clozapine -0.904
## 40 Xue 2017 478 Mouse MK-801 Risperidone 0.270
## ci_lb ci_ub
## 1 4.185 9.759
## 2 -3.235 -0.686
## 3 0.317 2.926
## 4 2.070 5.601
## 5 3.708 8.790
## 6 -0.990 0.388
## 7 -1.143 0.247
## 8 -1.012 0.662
## 9 -0.844 0.179
## 10 -0.932 0.057
## 11 -0.331 1.369
## 12 2.155 4.805
## 13 8.125 15.223
## 14 0.786 2.874
## 15 1.495 4.916
## 16 3.357 7.642
## 17 -0.825 1.138
## 18 3.208 7.362
## 19 -1.089 0.872
## 20 0.994 3.497
## 21 2.212 5.535
## 22 -1.101 1.274
## 23 -0.862 1.528
## 24 -0.715 1.693
## 25 -0.631 1.227
## 26 0.828 3.457
## 27 1.022 3.767
## 28 0.941 3.415
## 29 -0.180 1.736
## 30 -0.209 1.702
## 31 -1.445 1.040
## 32 -0.902 1.369
## 33 0.772 3.643
## 34 -0.126 2.298
## 35 -0.627 1.231
## 36 0.033 2.131
## 37 0.229 2.389
## 38 0.829 4.124
## 39 -2.205 0.397
## 40 -0.714 1.254Individual effect sizes included in the meta-analysis. This table lists all calculated Hedges’ g values and corresponding confidence intervals used in the analyses.
Session info
## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
## [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
## [5] LC_TIME=Portuguese_Brazil.utf8
##
## time zone: America/Sao_Paulo
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RColorBrewer_1.1-3 scales_1.4.0 stringr_1.5.1
## [4] forcats_1.0.1 ggalluvial_0.12.5 tidyr_1.3.1
## [7] ggplot2_4.0.0 orchaRd_2.1.3 clubSandwich_0.6.1
## [10] metafor_4.8-0 numDeriv_2016.8-1.1 metadat_1.2-0
## [13] Matrix_1.6-5 dplyr_1.1.4 readxl_1.4.5
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 beeswarm_0.4.0 xfun_0.52 bslib_0.9.0
## [5] lattice_0.22-6 mathjaxr_1.6-0 vctrs_0.6.5 tools_4.3.1
## [9] generics_0.1.4 sandwich_3.1-1 tibble_3.2.1 pkgconfig_2.0.3
## [13] S7_0.2.0 lifecycle_1.0.4 compiler_4.3.1 farver_2.1.2
## [17] textshaping_1.0.0 prettydoc_0.4.1 codetools_0.2-20 vipor_0.4.7
## [21] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.10 pillar_1.11.1
## [25] jquerylib_0.1.4 MASS_7.3-60.0.1 cachem_1.1.0 multcomp_1.4-28
## [29] nlme_3.1-164 tidyselect_1.2.1 digest_0.6.35 mvtnorm_1.3-3
## [33] stringi_1.8.7 purrr_1.0.2 labeling_0.4.3 splines_4.3.1
## [37] latex2exp_0.9.6 fastmap_1.2.0 grid_4.3.1 cli_3.6.2
## [41] magrittr_2.0.3 survival_3.5-8 TH.data_1.1-4 withr_3.0.2
## [45] ggbeeswarm_0.7.2 estimability_1.5.1 rmarkdown_2.30 emmeans_1.11.2-8
## [49] cellranger_1.1.0 ragg_1.3.3 zoo_1.8-13 evaluate_1.0.5
## [53] knitr_1.50 rlang_1.1.5 xtable_1.8-4 glue_1.8.0
## [57] rstudioapi_0.17.1 jsonlite_2.0.0 R6_2.6.1 systemfonts_1.2.2